Methods for predicting response to treatment

Described herein are methods for treating rheumatoid arthritis by determining whether a subject having rheumatoid arthritis will respond to an anti-TNF-alpha therapy based on the number of innate and adaptive immune cells in a sample from the subject prior to treatment.

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Description
CLAIM OF PRIORITY

This application is a national stage application under 35 U.S.C. § 371 of PCT Application No. PCT/2018/051606, filed Sep. 18, 2018, which claims priority to U.S. Provisional Application No. 62/560,628, filed Sep. 19, 2017, for which each of these applications are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

Described herein are methods for treating rheumatoid arthritis by determining whether a subject having rheumatoid arthritis will respond to an anti-TNF-alpha therapy based on the number of innate and adaptive immune cells in a sample from the subject.

BACKGROUND

Most patients initiating biologic therapy for rheumatoid arthritis are put on anti-TNF-alpha treatment as the first line treatment. However, approximately 30% of patients do not respond to anti-TNF-alpha treatment, and their disease often progresses before their non-response can be clinically determined. Although studies have been focused on understanding the biology underlying non-response in these patients, this remains an area of active investigation. As a result, new methods are needed for determining ahead of time whether a particular rheumatoid arthritis patient will respond to anti-TNF-alpha therapy, so that an effective drug that the patient is likely to respond to can be administered. This will help drive better treatment outcomes and reduce the burden on the healthcare system.

SUMMARY

The methods described herein enable the prediction of whether a subject having rheumatoid arthritis (RA) will respond to treatment using an anti-TNF-alpha therapy, e.g., treatment with an anti-TNF-alpha therapeutic biologic. The methods are based on observations made in comprehensive molecular profiling studies that identified differences in the innate and adaptive immune cell signatures of rheumatoid arthritis patients at a baseline time point prior to treatment with an anti-TNF-alpha therapy. These differences in immune cell signature profiles indicate that there are differences in the immune systems of patients that may influence whether the patients will respond to anti-TNF-alpha treatment within the first 3 months following therapy. In particular, the relative numbers of innate immune cells (e.g., neutrophils and monocytes) to adaptive immune cells (e.g., B cells and T cells) can be used predict whether a subject with rheumatoid arthritis is likely to respond to an anti-TNF-alpha therapy, and consequently aid in the development of an effective treatment plan for the subject, i.e., whether to administer an anti-TNF-alpha therapy based on whether the subject is likely to respond well. In some cases, the relative levels of innate immune cell signatures and/or adaptive immune cell signatures can be used to predict whether a subject with rheumatoid arthritis is likely to respond to an anti-TNF-alpha therapy. Thus, the methods described herein provide an improved approach for selecting rheumatoid arthritis patients for anti-TNF-alpha therapy or an alternative treatment other than an anti-TNF-alpha therapy (i.e., not an anti-TNF therapy), resulting in improved treatment outcomes for rheumatoid arthritis patients.

Described herein is a method for treating a patient with rheumatoid arthritis, comprising:determining whether the patient has a high ratio of innate immune cells to adaptive immune cells by: obtaining or having obtained a biological sample from the patient; and performing or having performed an assay on the biological sample to determine if the patient has a high ratio of innate immune cells to adaptive immune cells; and if the patient has a high ratio of innate immune cells to adaptive immune cells, then administering to the patient an anti-TNF therapeutic, and if the patient has a low ratio of innate immune cells to adaptive immune cells, then administering an rheumatoid arthritis treatment other than an anti-TNF therapeutic, thereby treating the patient.

Also described is a method for treating a patient with rheumatoid arthritis, comprising: detecting a ratio of innate immune cells to adaptive immune cells in a biological sample from a patient suffering from rheumatoid arthritis; and if the biological sample has a high ratio of innate immune cells to adaptive immune cells, then administering to the patient an anti-TNF therapeutic; and if the biological sample has a low ratio of innate immune cells to adaptive immune cells, then administering to the patient a rheumatoid arthritis treatment other than an anti-TNF therapeutic, thereby treating the patient.

Also described is a method of advising a treatment for rheumatoid arthritis, comprising: measuring a ratio of innate immune cells to adaptive immune cells in a biological sample from a patient suffering from rheumatoid arthritis; and advising a treatment comprising administration of an anti-TNF therapeutic if the ratio of innate immune cells to adaptive immune cells in the biological sample is high; and advising a treatment comprising administration of a rheumatoid arthritis treatment other than anti-TNF therapeutic if the ratio of innate immune cells to adaptive immune cells in the biological sample is low.

Also described is a method of advising a treatment of rheumatoid arthritis, comprising: selecting two or more patients suffering from rheumatoid arthritis who have not previously been treated with an anti-TNF therapeutic; measuring a ratio of innate immune cells to adaptive immune cells in biological samples collected from the two or more patients suffering from rheumatoid arthritis; advising a treatment of rheumatoid arthritis comprising administration of an anti-TNF therapeutic if the ratio of innate immune cells to adaptive immune cells in the biological sample is high; and advising a treatment of rheumatoid arthritis comprising administration of a rheumatoid arthritis treatment other than anti-TNF therapeutic if the ratio of innate immune cells to adaptive immune cells in the biological sample is low; wherein at least one of the two or more patients suffering from rheumatoid arthritis has a ratio of innate immune cells to adaptive immune cells that is low.

Also described A method of identifying a population of subjects with rheumatoid arthritis for treatment with an anti-TNF, comprising: selecting a population of subjects with rheumatoid arthritis who have not previously been treated with an anti-TNF; and identifying a subset of the population having a high ratio of innate immune cells to adaptive immune cells for treatment with an anti-TNF.

In various cases of all of the methods: a high ratio is a ratio above that found in rheumatoid arthritis patients in the lowest 25% of innate immune cell to adaptive immune cell ratios; a high ratio is a ratio above that found in rheumatoid arthritis patients in the lowest 20% of innate immune cell to adaptive immune cell ratios; a high ratio is a ratio above that found in rheumatoid arthritis patients in the lowest 15% of innate immune cell to adaptive immune cell ratios; a high ratio is a ratio above that found in rheumatoid arthritis patients in the lowest 10% of innate immune cell to adaptive immune cell ratios.

Also described is a method of treating patient suffering from rheumatoid arthritis, comprising: administering an anti-TNF therapeutic to a patient having a high ratio of innate immune cells to adaptive immune cells in a biological sample collected from the patient, thereby treating the patient.

Also described is a method of treating a patient suffering from rheumatoid arthritis, comprising: administering a therapeutic other than an anti-TNF therapeutic to a patient having a low ratio of innate immune cells to adaptive immune cells in a biological sample collected from the patient, thereby treating the patient.

Also described is a method for selecting a therapeutic for the treatment of rheumatoid arthritis in a subject, comprising:determining a ratio of innate immune cells to adaptive immune cells in a sample from a subject and if the proportion of innate immune cells is higher than the proportion of adaptive immune cells then selecting an anti-TNF therapeutic for the treatment of rheumatoid arthritis in the subject; or if the proportion of innate immune cells is lower than the proportion of adaptive immune cells then selecting an non-anti-TNF therapeutic for the treatment of rheumatoid arthritis in the subject; and memorializing the selection.

Also described is a method comprising selecting a therapeutic from the group consisting of an anti-TNF therapeutic and a non-anti-TNF therapeutic for the treatment of rheumatoid arthritis in a subject by determining a ratio of innate immune cells to adaptive immune cells in a sample from a subject, wherein if the proportion of innate immune cells is higher than the proportion of adaptive immune cells then selecting the anti-TNF therapeutic and if the proportion of innate immune cells is lower than the proportion of adaptive immune cells then selecting the non-anti-TNF therapeutic

Also described is a method of treating rheumatoid arthritis in a subject comprising: determining that a ratio of innate immune cells to adaptive immune cells in a sample from a subject is high; and administering an anti-TNF therapeutic.

Also described is a method of treating rheumatoid arthritis in a subject comprising:determining that a ratio of innate immune cells to adaptive immune cells in a sample from a subject is low; and administering a non-anti-TNF therapeutic to the subject.

In various embodiments of all of the methods: a low ratio is a ratio below that found in rheumatoid arthritis patients in the highest 75% of innate immune cell to adaptive immune cell ratios; a low ratio is a ratio above that found in rheumatoid arthritis patients in the highest 80% of innate immune cell to adaptive immune cell ratios; a low ratio is a ratio above that found in rheumatoid arthritis patients in the highest 85% of innate immune cell to adaptive immune cell ratios and a low ratio is a ratio above that found in rheumatoid arthritis patients in the highest 90% of innate immune cell to adaptive immune cell ratios.

In various embodiment of all of the methods: the step of determining whether the patient has a high ratio of innate immune cells to adaptive immune cells comprises determining one or more of: the ratio of neutrophils to white blood cells in the biological sample, the ratio of lymphocytes to white blood cells in the biological sample, and the ratio of neutrophils to lymphocytes in the biological sample; the anti-TNF therapeutic is an anti-TNF antibody; the anti-TNF therapeutic is selected from: infliximab, adalimumab, golimumab, certolizumab pegol and etanercept; the rheumatoid arthritis treatment other than an anti-TNF therapeutic is selected from the group consisting of: an anti-CD20 antibody, and anti-IL-6R antibody and a CTLA-4-Ig fusion; the rheumatoid arthritis treatment other than an anti-TNF therapeutic is selected from the group consisting of: abatacept, rituximab and tocilizumab; the step of determining whether the patient has a high ratio of innate immune cells to adaptive immune cells comprises determining the expression in the biological sample of one or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22; the patient is also administered methotrexate; the patient is administered the anti-TNF therapeutic and is not administered methotrexate; the innate immune cells comprise neutrophils and monocytes and the adaptive immune cells comprise B cells and T cells; the step of determining one or more of: the ratio of neutrophils to white blood cells in the biological sample, the ratio of lymphocytes to white blood cells in the biological sample, and the ratio of neutrophils to lymphocytes in the biological sample comprises performing a blood cell count; the step of determining the expression in the biological sample of one or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22 comprises FACS analysis; step of determining the expression in the biological sample of one or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22; the step of determining whether the patient has a high ratio of innate immune cells to adaptive immune cells comprises determining the expression in the biological sample of two or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22.

In various embodiment of all of the methods: the step of determining whether the patient has a high ratio of innate immune cells to adaptive immune cells comprises determining the expression in the biological sample of three or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22; the step of determining whether the patient has a high ratio of innate immune cells to adaptive immune cells comprises determining the expression in the biological sample of four or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, ratio of innate immune cells to adaptive immune cells comprises determining the log of the ratio of neutrophils to lymphocytes (Ln(NRL)) in the biological sample, and administering an anti-TNF therapeutic if the value of Ln(NLR) is greater than about 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1,2, 1.3, 1.4, 1.5, 1.6, or 1.7.

In various embodiment of all of the methods: the step of determining whether the patient has a high ratio of innate immune cells to adaptive immune cells comprises determining the expression in the biological sample of one (e.g., 2, 3, 4, 5, 6, 7, 8, 9 or 10 or more of the genes in any of FIGS. 6, 7 and 8 associated with an innate immune response and determining the expression in the biological sample of one or more of the genes in FIGS. 6, 7 and 8 associated with an adaptive immune response.

Also described is a method for treating a patient with rheumatoid arthritis, comprising: determining whether the patient has a high ratio of innate immune cells to adaptive immune cells by: obtaining or having obtained a biological sample from the patient; and performing or having performed an assay on the biological sample to determine if the patient has a high ratio of innate immune cells to adaptive immune cells; and if the patient has a high ratio of innate immune cells to adaptive immune cells, then administering to the patient an anti-innate immune cell therapeutic agent, and if the patient has a low ratio of innate immune cells to adaptive immune cells, then administering an rheumatoid arthritis treatment other than an anti-innate immune cell therapeutic agent, thereby treating the patient.

Also described is a method treating a patient with rheumatoid arthritis, comprising: detecting a ratio of innate immune cells to adaptive immune cells in a biological sample from a patient suffering from rheumatoid arthritis; and if the biological sample has a high ratio of innate immune cells to adaptive immune cells, then administering to the patient an anti-innate immune cell therapeutic agent; and if the biological sample has a low ratio of innate immune cells to adaptive immune cells, then administering to the patient a rheumatoid arthritis treatment other than an anti-innate immune cell therapeutic agent, thereby treating the patient.

Also described is a method advising a treatment for rheumatoid arthritis, comprising: measuring a ratio of innate immune cells to adaptive immune cells in a biological sample from a patient suffering from rheumatoid arthritis; and advising a treatment comprising administration of an anti-innate immune cell therapeutic agent if the ratio of innate immune cells to adaptive immune cells in the biological sample is high; and advising a treatment comprising administration of a rheumatoid arthritis treatment other than anti-innate immune cell therapeutic agent if the ratio of innate immune cells to adaptive immune cells in the biological sample is low.

Also described is a method advising a treatment of rheumatoid arthritis, comprising: selecting two or more patients suffering from rheumatoid arthritis who have not previously been treated with an anti-TNF therapeutic; measuring a ratio of innate immune cells to adaptive immune cells in biological samples collected from the two or more patients suffering from rheumatoid arthritis; advising a treatment of rheumatoid arthritis comprising administration of an anti-innate immune cell therapeutic agent if the ratio of innate immune cells to adaptive immune cells in the biological sample is high; and advising a treatment of rheumatoid arthritis comprising administration of a rheumatoid arthritis treatment other than anti-innate immune cell therapeutic agent if the ratio of innate immune cells to adaptive immune cells in the biological sample is low; wherein at least one of the two or more patients suffering from rheumatoid arthritis has a ratio of innate immune cells to adaptive immune cells that is low.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a bar graph showing changes in whole-blood gene expression after 3 months of anti-TNF treatment (MO3) relative to baseline (BL) for Cohort 1 (C1) and Cohort 2 (C2), plotted according to statistical significance (distribution of p-values).

FIG. 1B is a graph showing changes in gene expression after 3 months of anti-TNF treatment (MO3) relative to baseline (BL), with gene sets related to particular cell types (myeloid cells, B cells, and T cells) highlighted, plotted for Cohort 1 (C1) versus Cohort 2 (C2).

FIG. 1C is a graph showing changes in gene expression after 3 months of anti-TNF treatment relative to baseline (MO3-BL) for genes related to neutrophils for C1 (left graph) and C2 (right graph).

FIG. 1D is a graph showing the changes in gene expression after 3 months of anti-TNF treatment relative to baseline for genes related to different cell types (neutrophils, B-cells, CD4 cells, CD8 cells, monocytes, and NK cells) using four publicly available cell-type-specific datasets as a reference (GSE22886, GSE28490, GSE28491, and GSE60424) for C1 (left panel) and C2 (right panel).

FIG. 1E is a bar graph showing changes in protein expression after 3 months of anti-TNF treatment (MO3) relative to baseline (BL) for Cohort 1 (C1) and Cohort 2 (C2), plotted according to statistical significance (distribution of p-values).

FIG. 1F is a graph showing changes in protein expression in plasma after 3 months of anti-TNF treatment (MO3) relative to baseline (BL), with acute phase proteins highlighted, plotted for Cohort 1 (C1) versus Cohort 2 (C2).

FIG. 2A includes graphs showing pair-wise comparisons of the changes in gene expression after 3 months of anti-TNF treatment (MO3) relative to baseline (BL) between good and poor responders to anti-TNF treatment in C1 and C2.

FIG. 2B includes graphs showing comparisons in the differences in protein expression levels after 3 months of anti-TNF treatment relative to baseline (MO3-BL) between the good and poor responders in C1 (left panel), and the good and poor responders in C2 (right panel).

FIG. 2C includes graphs showing differences in the protein expression levels after 3 months of anti-TNF treatment relative to baseline (MO3-BL) for biological pathways (according to gene ontology (GO) categories) that are modulated after anti-TNF expression in the good and in the poor responders in C1 (left panel) and C2 (right panel).

FIG. 3A is a graph showing the differences in gene expression between the good responders and poor responders prior to anti-TNF treatment, plotted according to statistical significance (distribution of p-values).

FIG. 3B is a graph showing the differences in baseline gene expression levels between the good responders and poor responders to anti-TNF treatment, plotted for Cohort 1 (C1) versus Cohort 2 (C2).

FIG. 3C is a graph showing the differences in baseline gene expression levels between the good responders and poor responders to anti-TNF treatment for a subset of genes that exhibited the greatest expression variability between the good and poor responders, plotted for Cohort 1 (C1) versus Cohort 2 (C2).

FIG. 4A is a graph showing the average baseline expression of subsets of genes (subsets of the top 10 genes, top 50 genes, or top 250 genes) that are predominantly expressed in particular cell types (neutrophils, B-cells, CD4 cells, CD8 cells, monocytes, and NK cells) in good responders compared to poor responders in C1 (left panel) and C2 (right panel).

FIG. 4B is a graph showing the average baseline expression of subsets of genes (subsets of the top 10 genes, top 50 genes, or top 250 genes) that are predominantly expressed in particular cell types (neutrophils, B-cells, CD4 cells, CD8 cells, monocytes, and NK cells) in good responders compared to poor responders in five rheumatoid arthritis datasets (GSE12051, GSE33377, GSE42296, GSE58795, and GSE15258).

FIG. 5 is a graph showing the correlation between average baseline expression profiles of subsets of genes that are predominantly expressed in particular cell types (neutrophils, B-cells, CD4 cells, CD8 cells, monocytes, and NK cells) in Cohort 1 (C1) and Cohort 2 (C2), compared to corresponding cell counts and their ratios.

FIG. 6 is a list of genes that can be used as markers of innate immune cells (higher expression in neutrophils and monocytes versus T cells and B cells) and genes that can be used as markers of adaptive immune cells (higher expression in T cells and B cells versus neutrophils and monocytes).

FIG. 7 is a list of top 10 genes associated with innate immune response cells (10 from neutrophils and 10 from monocytes) and top 10 genes associated with adaptive immune response cells (10 from B cells, 10 from CD4+ cells, 10 from CD8+ cells and 10 from NK cells).

FIG. 8 is a list of top 50 genes associated with innate immune response cells (50 from neutrophils and 50 from monocytes) and top 50 genes associated with adaptive immune response cells (50 from B cells, 50 from CD4+ cells, 50 from CD8+ cells and 50 from NK cells).

FIG. 9 is a list of top 200 genes associated with innate immune response cells (50 from neutrophils and 200 from monocytes) and top 20 genes associated with adaptive immune response cells (200 from B cells, 200 from CD4+ cells, 200 from CD8+ cells and 200 from NK cells).

 DETAILED DESCRIPTION

Although anti-TNF therapies have provided significant benefits to rheumatoid arthritis (RA) patients, an absence of response in 30% of patients to anti-TNF therapy and an inability to prospectively identify those RA patients that fail to respond to treatment (i.e., non-responders or poor responders) prior to administering an anti-TNF therapy, represents a key unmet medical need. The methods disclosed herein can be used to determine whether a subject with rheumatoid arthritis is likely to respond to treatment with an anti-TNF-alpha therapy. In some embodiments, this determination is used to select a rheumatoid arthritis subject for treatment with an anti-TNF-alpha therapy, e.g., an anti-TNF-alpha therapeutic biologic (e.g., adalimumab. infliximab, golimumab, certolizumab pegol and/or etanercept). In some embodiments, this determination is used to select a rheumatoid arthritis subject for treatment with an innate immune cell targeting agent (e.g., an anti-TNF-alpha therapeutic biologic). In some embodiments, this determination is used to select a rheumatoid arthritis subject for treatment with a therapy that is not an anti-TNF-alpha therapeutic agent (i.e., is other than an anti-TNF-alpha therapeutic, e.g., a second-line biologic with efficacy in RA patients who fail to respond to anti-TNF therapy, such as biologics that target B and/or T cell responses (e.g., rituximab (anti-CD20), abatacept (CTLA-4-Ig), or tocilizumab (anti-IL-6R)). In some embodiments, this determination is used to select a rheumatoid arthritis subject for treatment with a therapy that is any adaptive immune cell targeting agent (e.g., not an anti-TNF-alpha therapeutic biologic).

The methods disclosed herein are based in part on the observation that innate immune cells are present in larger numbers (in comparison to adaptive immune cells) and/or their molecular signatures are present at higher levels in samples collected from rheumatoid arthritis patients who are more likely to respond to treatment with anti-TNF-alpha therapy prior to the administration of the anti-TNF-alpha therapy. By contrast, adaptive immune cells are present in larger numbers (in comparison to innate immune cells) and/or their molecular signatures are present at higher levels in samples collected from rheumatoid arthritis patients who are less likely to respond to treatment with anti-TNF-alpha therapy prior to the administration of the anti-TNF-alpha therapy. The relative numbers and/or signature levels of innate immune cells versus adaptive immune cells in a sample collected from a subject with rheumatoid arthritis can be used to determine whether the subject is likely to respond to an anti-TNF-alpha therapy before a therapy for the disease is selected and administered to the subject.

In some implementations, the disclosure relates to methods for treating a subject with rheumatoid arthritis (e.g., a patient suffering from RA) with an anti-TNF therapeutic that includes determining the ratio of innate immune cells to adaptive immune cells in a sample from the subject, and then determining what treatment to administer based on ratio value. In some embodiments, the ratio is innate immune cells to adaptive immune cells ratio. In some embodiments, the ratio is neutrophils to lymphocytes ratio (NLR). In some embodiments, the ratio is neutrophils to white blood cells ratio (NWR). In some embodiments, the ratio is lymphocytes to white blood cells ratio (LWR). In some embodiments, if the ratio of innate immune cells to adaptive immune cells in a sample from the subject is high, then an anti-TNF therapeutic is administered to the subject. In some embodiments, if the ratio of innate immune cells to adaptive immune cells in a sample from the subject is not high, then an rheumatoid arthritis treatment other than an anti-TNF therapeutic is administered to the subject.

In some cases, the innate immune cells are neutrophils and monocytes, such that the number of neutrophils and/or monocytes is determined in an RA patient sample prior to selection of an RA therapy. In some cases, the adaptive immune cells are B cells, T cells (e.g., CD4 cells, CD8 cells), such that the number of B cells and/or T cells is determined in an RA patient prior to selection of an RA therapy. In some embodiments, a ratio of any one or more innate immune cell type (e.g., neutrophils and/or monocytes) to any one or more adaptive cell type (e.g., B cells or T cells) is determined in an RA patient sample to predict responsiveness to anti-TNF therapy. In some embodiments, if the ratio of neutrophils and/or monocytes to B cells and/or T cells is above a pre-defined threshold (e.g., is high relative to a reference ratio), then one can consider treating the RA patient with an anti-TNF therapeutic or an innate immune cell targeting agent. In some embodiments, the ratio of neutrophils to lymphocytes (NLR) can be determined. If the NLR is above a pre-defined threshold (e.g., is high relative to a reference ratio), then one can consider treating the RA patient with an anti-TNF therapeutic or an innate immune cell targeting agent.

In some embodiments, the ratio of neutrophils to white blood cells (NWR) can be determined. If the NWR is above a pre-defined threshold, then one can consider treating the RA patient with an anti-TNF therapeutic or an innate immune cell targeting agent. In some embodiments, the ratio of lymphocytes to white blood cells (LWR) can be determined. If the LWR is above a pre-defined threshold, then one can consider treating the RA patient with a therapeutic other than an anti-TNF therapeutic or an adaptive immune cell targeting agent. In some embodiments, “white blood cells” can include a mixture of innate and adaptive immune cells. In some embodiments, white blood cells can include any two or more of neutrophils, lymphocytes, monocytes, eosinophils, and/or basophils. In some embodiments, white blood cells can include neutrophils, lymphocytes, monocytes, eosinophils, and/or basophils. In some embodiments, over 20% of the cells in white blood cells can be neutrophils and lymphocytes, e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95% or more of the cells are neutrophils and lymphocytes.

In some embodiments, determining the ratio of innate immune cells to adaptive immune cells in a sample from the subject with RA can include determining the ratio of neutrophils to white blood cells in the biological sample, the ratio of lymphocytes (B cells and/or T cells) to white blood cells in the biological sample, and/or the ratio of neutrophils to lymphocytes in the biological sample. In some embodiments, determining the ratio of innate immune cells to adaptive immune cells in a sample from the subject with RA includes determining the ratio of neutrophils to white blood cells in the biological sample, the ratio of lymphocytes (B cells and/or T cells) to white blood cells in the biological sample, or the ratio of neutrophils to lymphocytes in the biological sample. In some embodiments, determining the ratio of innate immune cells to adaptive immune cells in a sample from the subject with RA includes one or more of determining the ratio of neutrophils to white blood cells in the biological sample, the ratio of lymphocytes (B cells and/or T cells) to white blood cells in the biological sample, and/or the ratio of neutrophils to lymphocytes in the biological sample.

In some embodiments, the ratio of innate immune cells to adaptive immune cells is determined in a sample from the subject with RA before an anti-TNF therapeutic is administered to the subject. In some embodiments, the ratio of innate immune cells to adaptive immune cells is determined in a sample from the subject with RA shortly before or at the same time that an anti-TNF therapeutic is administered to the subject. In some embodiments, the ratio of innate immune cells to adaptive immune cells is determined in a sample from the subject with RA before an RA therapeutic is administered to the subject, e.g., an RA therapeutic other than an anti-TNF therapeutic. In some embodiments, the ratio of innate immune cells to adaptive immune cells is determined in a sample from the subject with RA shortly before or at the same time that an RA therapeutic is administered to the subject, e.g., an RA therapeutic other than an anti-TNF therapeutic.

In some embodiments, the ratio of innate immune cells (e.g., neutrophils) to adaptive immune cells (e.g., adaptive immune cells) is compared to a reference ratio of innate immune cells to adaptive immune cells. The reference ratio can be based on the ratio of innate immune cells to adaptive immune cells in a sample from a population of subjects with RA that yields a certain likelihood of response to anti-TNF therapeutic (e.g., and anti-TNF antibody). When the ratio of innate immune cells to adaptive immune cells in the subject sample is considered moderate or high relative to the reference ratio, then the subject is considered more likely to respond to an anti-TNF therapeutic, i.e., the anti-TNF therapeutic will cause a reduction in RA symptoms in the subject. When the ratio of innate immune cells to adaptive immune cells in the subject sample is considered low relative to the reference ratio, then the subject is considered less likely to respond to an anti-TNF therapeutic. In some embodiments, the reference ratio is the lowest 25% of the ratios of innate immune cells to adaptive immune cells in a population of RA patients. In some embodiments, a reference ratio can be the ratio above which there is at least an 60%, 65%, 70%, 75% or greater chance that a patient will respond the therapy.

In some embodiments, the ratio of innate immune cells to adaptive immune cells in a sample from a subject with RA is compared to the ratios of innate immune cells to adaptive immune cells in a population of subjects with RA. In some embodiments, if the ratio of innate immune cells to adaptive immune cells in sample from a subject with RA is higher than the lowest 25% of the ratios of innate immune cells to adaptive immune cells in the population of subjects with RA, then the subject is likely or more likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with anti-TNF therapeutic. In some embodiments, if the ratio of innate immune cells to adaptive immune cells in sample from a subject with RA is lower than the lowest 25% of the ratios of innate immune cells to adaptive immune cells in the population of subjects with RA, then the subject is unlikely or less likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with a therapeutic other than an anti-TNF therapeutic (i.e., a therapeutic that is not an anti-TNF therapeutic).

In some embodiments, the ratio of neutrophils and/or monocytes to B cells and/or T cells in a sample from a subject with RA is compared to the ratios of neutrophils and/or monocytes to B cells and/or T cells in a population of subjects with RA. In some embodiments, if the ratio of neutrophils and/or monocytes to B cells and/or T cells in sample from a subject with RA is higher than the lowest 25% of the ratios of neutrophils and/or monocytes to B cells and/or T cells in the population of subjects with RA, then the subject is likely or more likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with anti-TNF therapeutic. In some embodiments, if the ratio of neutrophils and/or monocytes to B cells and/or T cells in sample from a subject with RA is lower than the lowest 25% of the ratios of neutrophils and/or monocytes to B cells and/or T cells in the population of subjects with RA, then the subject is unlikely or less likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with a therapeutic other than an anti-TNF therapeutic (i.e., a therapeutic that is not an anti-TNF therapeutic).

In some embodiments, the NLR in a sample from a subject with RA is compared to the NLRs in a population of subjects with RA. In some embodiments, if the NLR in sample from a subject with RA is higher than the lowest 25% of the NLRs in the population of subjects with RA, then the subject is likely or more likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with anti-TNF therapeutic. In some embodiments, if the NLR in sample from a subject with RA is lower than the lowest 25% of the NLRs in the population of subjects with RA, then the subject is unlikely or less likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with a therapeutic other than an anti-TNF therapeutic (i.e., a therapeutic that is not an anti-TNF therapeutic).

In some embodiments, the ratio of neutrophils to white blood cells in a sample from a subject with RA is compared to the ratios of neutrophils to white blood cells in a population of subjects with RA. In some embodiments, if the ratio of neutrophils to white blood cells in sample from a subject with RA is higher than the lowest 25% of the ratios of neutrophils to white blood cells in the population of subjects with RA, then the subject is likely or more likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with anti-TNF therapeutic. In some embodiments, if the ratio of neutrophils to white blood cells in sample from a subject with RA is lower than the lowest 25% of the ratios of neutrophils to white blood cells in the population of subjects with RA, then the subject is unlikely or less likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with a therapeutic other than an anti-TNF therapeutic (i.e., a therapeutic that is not an anti-TNF therapeutic).

In some embodiments, the NWR in a sample from a subject with RA is compared to the NWRs in a population of subjects with RA. In some embodiments, if the NWR in sample from a subject with RA is higher than the lowest 25% of the NWRs in the population of subjects with RA, then the subject is likely or more likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with anti-TNF therapeutic. In some embodiments, if the NWR in sample from a subject with RA is lower than the lowest 25% of the NWRs in the population of subjects with RA, then the subject is unlikely or less likely to respond to an anti-TNF therapeutic and the subject should be considered treatment with a therapeutic other than an anti-TNF therapeutic (i.e., a therapeutic that is not an anti-TNF therapeutic),In some embodiments, the ratio of innate immune cells to adaptive immune cells is determined as the log of the ratio of neutrophils to lymphocytes in a sample from a subject with RA (Ln(NLR). In some embodiments, a subject with RA is administered an anti-TNF therapeutic when the Ln(NLR) is greater than 0.6 e.g., the Ln(NLR) is 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, or 3.0 or more. In some embodiments, a subject with RA is administered an anti-TNF therapeutic when the Ln(NLR) is between 0.6 and 3.0, e.g., 0.6 to 2.0, 1.0 to 2.0, 1.3 to 1.6, 1.2 to 1.8, 1.2 to 2.2, 1.5 to 2.5, 1.3 to 2.3, 1.5 to 2.5, or 2.0 to 3.0.

In some embodiments, a subject with RA is administered a therapeutic other than anti-TNF (i.e., a therapeutic that is not anti-TNF) when the Ln(NLR) is less than 0.6, e.g., the Ln(NLR) is 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, or 0.05 or less. In some embodiments, a subject with RA is administered a therapeutic other than anti-TNF when the Ln(NLR) is between 0.1 and 0.59, e.g., 0.1 to 0.5, 0.2 to 0.59, or 0.2 to 0.4.

In some embodiments, a subject with RA can be selected for anti-TNF treatment based on an assessment of the number of innate immune cells and/or adaptive immune cells in a sample, e.g., a blood sample, collected from the subject prior to anti-TNF treatment. Any methods known in the art for identifying and counting immune cells in a sample, e.g., a clinical blood sample, can be used to determine the number of innate and/or adaptive immune cells in the sample collected from the subject with RA. The number of innate and/or adaptive immune cells can be counted in the sample by any suitable clinical cell counting methodology known in the art. In some embodiments, the types and numbers of immune cells in the sample is determined by a blood cell count, e.g., a complete blood count (CBC) or differential blood cell count, using methods known in the art. In some embodiments, the types and numbers of immune cells in the sample can be determined by FACS analysis of cells in the sample, e.g., a blood sample.

In some embodiments, a subject with RA can be selected for anti-TNF treatment based on an assessment of the levels of molecular signatures for innate immune cells types and/or adaptive immune cell types in a sample, e.g., a blood sample, collected from the subject prior to anti-TNF treatment. In some embodiments, the molecular signature can be the gene expression level of one or more genes whose expression is closely associated with an innate or adaptive immune cell type. In some embodiments, the molecular signature can be the protein expression level of one or more proteins whose expression is closely associated with an innate or adaptive immune cell type. Any methods known in the art for measuring and analyzing gene or protein expression can be used to assess the molecular signature of innate and adaptive immune cells, including, but not limited to, FACS analysis, polymerase chain reaction (e.g., RT-PCR of mRNA), microarrays, mass spectrometry, proteomics, etc.

In some embodiments, determining the ratio of innate immune cells to adaptive immune cells in a sample from the subject with RA (e.g., in determining whether the subject has a high ratio of innate immune cells to adaptive immune cells) can include determining the expression in the sample of one or more genes in FIG. 6, e.g., one or more genes in FIG. 6 associated with an innate immune response and/or an adaptive immune response. In some embodiments, determining the ratio of innate immune cells to adaptive immune cells in a sample from the subject with RA (e.g., in determining whether the subject has a high ratio of innate immune cells to adaptive immune cells) can include determining the expression in the sample of one or more genes in FIG. 6 associated with an innate immune response and/or one or more genes in FIG. 6 associated with an adaptive immune response, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 300, 35, 400, or 420 or more genes in FIG. 6.

In some embodiments, determining the ratio of innate immune cells to adaptive immune cells in a sample (e.g., a blood sample) from the subject with RA (e.g., in determining whether the subject has a high ratio of innate immune cells to adaptive immune cells) can include determining the expression of one or more of CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22 in the sample, e.g., determining the expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 of CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22 in the sample. In some embodiments, the gene expression of CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and/or CD22 is determined. In some embodiments, the protein expression of CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22 is determined. In some embodiments, the anti-TNF therapeutic can be an anti-TNF antibody. In some embodiments, the anti-TNF therapeutic is infliximab, adalimumab, golimumab, certolizumab pegol or etanercept. In some embodiments, the subject is administered methotrexate with an anti-TNF therapeutic. In some embodiments, the subject is not administered methotrexate with an anti-TNF therapeutic.

In some embodiments, the rheumatoid arthritis treatment other than an anti-TNF therapeutic (i.e., the therapeutic that is not anti-TNF) is an anti-CD20 antibody, an anti-IL-6R antibody or a CTLA-4-Ig fusion. In some embodiments, the rheumatoid arthritis treatment other than an anti-TNF therapeutic (i.e., the therapeutic that is not anti-TNF) is abatacept, rituximab or tocilizumab.

As used herein, the term “biological sample” or “sample” refers to a sample obtained, collected, or derived from a subject. The sample can include any bodily fluid (e.g., blood, whole blood, plasma, serum, mucus secretions, urine, sputum, lymph fluids, gynecological fluids, cystic fluid, cerebrospinal fluid, fluids collected from bronchial lavage, or saliva), cell, tissue, feces, or cell extracts from a subject.

EXAMPLES

The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

Example 1 Materials and Methods

Study Design and Sample Selection Criteria

A comprehensive molecular profiling study of rheumatoid arthritis (RA) patients starting anti-TNF-alpha therapy (or “anti-TNF” therapy or treatment) was conducted. Samples were collected and profiled from biologic naive RA patients being treated with anti-TNF-alpha therapy in combination with methotrexate (MTX) at two time points: first at a time point prior to initiating anti-TNF-alpha therapy (the “baseline” time point) and then again 3 months after treatment with anti-TNF-alpha therapy. The aim of the study was to understand the molecular mechanisms (other than drug neutralization) that affect clinical response to anti-TNF-alpha, and to identify markers that could be used to predict, prior to administering anti-TNF treatment (at baseline), which RA patients will likely exhibit a good or moderate response to anti-TNF treatment (“responders”, “good responders”, or “moderate responders”) versus those RA patients that will likely exhibit no response or a poor response to anti-TNF treatment (“poor responders” or “non-responders”).

Rheumatoid arthritis (RA) patient samples were obtained, and samples (whole blood and plasma) from RA patients that were biologic naive (i.e., received no prior treatment with a biologic agent), were initiating treatment with an anti-TNF therapeutic (either adalimumab or infliximab) in conjunction with methotrexate (MTX), and had no or stable low dose prednisone (<5 mg) were selected. Response of each RA patient to anti-TNF therapy at 3 months was evaluated using European League Against Rheumatism (EULAR) criteria. Patients were included in the study cohorts only if a minimum level of anti-TNF therapeutic (Humira® (adalimumab) or Remicade® (infliximab)) was detected in the 3 month plasma sample by a drug specific ELISA to assure drug exposure. Patients with drug levels of less than 800 ng/mL were excluded.

Patients Characteristics

Samples from RA patients were selected and split in two independent cohorts of 40 RA patients (Cohort 1 (C1)) and 36 RA patients (Cohort 2 (C2)) for the molecular profiling study. All patients in both cohorts were biologic-naive and undergoing treatment with methotrexate (MTX). Table 1 provides the demographic and clinical information for good and poor responders in Cohorts 1 and 2. Based on assessment of EULAR improvement criteria, 52.5% of patients (21 patients) from C1 were determined to be non-responders [NR] (or “poor” responders) and 47.5% of patients (19 patients) were moderate/good responders [R], while 41.7% of patients (15 patients) from C2 were determined to be non-responders and 58.3 moderate/good responders (21 patients). Poor responders exhibited higher levels of tender joint counts, Disease Activity Score 28-joint count C reactive protein (DAS28-CRP) at baseline, and, as a group, exhibited a lower percentage of CCP- and RF-positive subjects. Although samples were selected from both cohorts to match clinical and demographic measures across multiple covariates, a difference in significant smoking status was observed, due to a higher frequency of smokers in good responders in C1, compared to C2. Good responders in C2 exhibited higher swollen 28-joint count (SJC28) and tender joint counts at baseline, DAS28-CRP at baseline, and poor responders from C2 showed higher ln(CRP) at baseline and longer RA duration than poor responders from C1. Although these differences between the cohorts may affect the comparability of the two cohorts at the molecular level, none of these results reached statistical significance (see Table 1).

TABLE 1 Demographic and clinical information for good and poor responders in Cohorts 1 and 2. Cohort 1 Cohort 2 Good Poor p Good Poor p N 19 21 N/A 21 15 N/A Female, N (%)‡ 15 (79) 19 (90) 0.4 16 (76) 12 (80)  1 Age, mean (SD) 54 (13) 56 (13) 0.58 55 (12) 51 (9.9)  0.31 White, N (%) 17 (89) 14 (67) 0.13 19 (90) 13 (87)  1 Non-smoker, N (%)  8 (42) 14 (67) 0.2 14 (67) 6 (40) 0.18 Current or previous 11 (58)  7 (33) 0.2  7 (33) 6 (40) 0.74 smoker, N (%) Remicade, N (%)  8 (42)  9 (43) 1  6 (29) 8 (53) 0.18 Humira, N (%) 11 (58) 12 (57) 1 15 (71) 7 (47) 0.18 SJC28 [BL], mean (SD) 6.7 (3.7) 9.1 (5.5) 0.12 9.6 (5.5) 8.7 (4.9)  0.62 TJC28 [BL], mean (SD)*  9 (6.2)  15 (8.3) 0.015  11 (6.7) 14 (5.7)  0.31 ln(CRP) [BL], mean (SD) 1.6 (1.6) 1.2 (1.8) 0.49 1.5 (1.4) 1.8 (1.1)  0.54 DAS28CRP [BL], mean  4.5 (0.78)  5.2 (0.94) 0.014  4.8 (0.83) 5.2 (0.66) 0.094 (SD)* DAS28CRP [BL-MO3], 2.7 (0.8) 0.095 (0.33)  4.7e−16  2.9 (0.86) 0.0073 (0.67)   2.2e−12 mean (SD)§ RA duration, mean (SD)* 5.4 (7.5) 1.9 (1.7) 0.043  5 (6.5) 7.2 (8.3)  0.39 RF+, N (%) 16 (84) 12 (57) 0.089 16 (76) 8 (53) 0.18 CCP+, N (%)* 16 (84)  8 (38) 0.0041 16 (76) 6 (40) 0.032 ‡Numbers in brackets after each attribute represent percentages or standard deviation (SD) of that attribute, as indicated. *Difference between good and poor responders at baseline for this attribute is statistically significant (p < 0.05) in at least one of the cohorts. §DAS28CRP [BL-MO3] reflects the change in DAS28CRP score from baseline to month 3. Therefore, this attribute is a metric of response, and is expected to be different between good and poor responders.

Sample Handling, Processing, and Analysis

Whole-blood RNA samples (PAXgene) and plasma samples collected prior to initiating anti-TNF therapy (baseline) and following 3 months of anti-TNF treatment from the patients selected in each cohort were profiled using different technologies (RNAseq, proteomics and targeted glycopeptide analysis). Samples from each cohort were randomized with respect to study factors related to sample handling, processing and data acquisition (e.g. shotgun proteomics run order, RNA extraction, NGS sequencing batches, etc.). Cohort 2 samples were analyzed independently from Cohort 1 samples, and around 12 months after the Cohort 1 samples were analyzed.

Plasma Sample Processing

De-identified plasma samples were obtained for shotgun proteomic analysis. Plasma ID numbers were assigned at random to all plasma samples. Samples were then processed in the order of plasma ID numbers to insure minimal bias due to run order. Samples were processed and run as sets of 20 samples. A normal human plasma control (obtained from Sigma-Aldrich) was included with each set. Plasma samples were first depleted of the top 14 most abundant proteins using Multiple Affinity Removal System 14 (MARS-14), an immunoaffinity, HPLC-based methodology. Removal of high abundant proteins allows for the detection of medium to low abundant proteins by shotgun proteomics. A bicicinchoninic acid (BCA) assay was then performed to determine protein concentration.

Proteomics Analysis by LC-MS/MS

For each sample, 50 μg of total protein was aliquoted for digestion using trypsin/Lys-C. The resulting peptide mixtures were separated using an Ultimate 3000 RSLC nano system. Peptides were loaded onto an Acclaim PepMap RSLC Nano trap column (5 μm particle size, 20 mm×100 um) at 5 μLmin−1 flow rate and resolved on the basis of hydrophobicity using an EASY-Spray Acclaim PepMap RSLC C18 column. MS analyses were performed on Orbitrap Velos Pro in the positive-ion mode using an EASY-Spray nano-source. RAW files from the mass spectrometer were searched using Sequest HT as part of Proteome Discoverer 1.4 mass informatics software package. Files were searched against the human Uniprot database (including protein isoforms) and then opened as a multiconcensus report (5% peptide-level false discovery rate). Results were then exported into Microsoft Excel for further data analysis and normalized to total PSM for each sample to account for sample-to-sample variation.

Targeted Glycopeptide Analysis of Shed Fc receptors in Plasma by LC-MS/MS Analysis

Soluble FcγRs were isolated from 50 μL of plasma. Proteins were immunoprecipitated using biotinylated goat polyclonal antibodies against human FcγRIII (R&D Systems BAF1597) and human FcγRII (R&D Systems BAF1330). Marker peptides for polymorphic variants of both FcγRIIIb and FcγRIIa, as well as glycosylation of FcγRIII N45, were characterized using a chymotryptic digestion (Sequencing Grade Promega V1061). The peptides and glycopeptides were analyzed by nano LC-MS/MS on a Dionex Ultimate 3000 nano RSLC coupled to a QExactive mass spectrometer (ThermoFisher Scientific) equipped with and EasySpray nano-LC source (ThermoFisher Scientific). Peptides were separated on an EasySpray C18 column (0.75×250mm 2 μm particle size). A targeted nLC-MS/MS method was applied for the quantitation of site specific glycosylation as well as assignment of allelic variants based on peptide sequence information. The quadrapole isolation width was set to ±1 Da for the isolation of the parent ion of each of the species. Targeted species were quantified based on the extracted ion abundance for the peptide+GlcNAc fragment. The abundance was determined for each species relative to the summed extracted ion area for each site of glycosylation.

RNA Preparation and NGS Sequencing (RNA-Seq)

RNA was extracted from human whole blood samples preserved in PAXgene tubes (Qiagen). RNA extraction was performed according to the PAXGene Blood miRNA kit protocol (C1) or PAXGene Blood RNA kit protocol (C2) using the QIAcube instrument (Qiagen). RNA concentration was measured by absorbance at 260 nm, and RNA quality was measured by the Agilent TapeStation and Agilent Bioanalyzer. Libraries were prepared for RNAseq analysis with the Apollo 324 system from WaferGen Biosystems using the WaferGen Prep-X Directional RNA-Seq kit (C1) or Illumina's TruSeq Stranded mRNA Library Preparation Kit (C2) according to manufacturer's protocols. Libraries were sequenced on an Illumina HiSeq 2500 for 40×40 bases (C1), and 75×75 bases (C2), in paired end, high output mode.

FASTQ files were mapped to human reference (UCSC hg19) genome using two pass STAR alignment. QC metrics of resulting BAM files were obtained using RNAseQC. Gene counts were generated by featureCounts software program.

Data Analysis

All analyses of differential gene and protein expression were performed using limma-voom methodology. Multiple test correction for genome-wide assays (RNA-seq, shotgun proteomics) were performed using the Benjamini-Hochberg procedure. Non-parametric (Spearman's) rank correlation was used for assessing global concordance of gene/protein-level differences throughout. The statistical significance of correlations and counts of genes or proteins passing significance cutoffs where applicable was estimated by permutation. The results of such tests were deemed statistically significant if more extreme (by absolute value) statistic (e.g. correlation coefficient, protein count, etc.) was obtained in fewer than 5% of permutations. Adjustment for confounding factors, such as between subject variability, sample processing order in shotgun proteomics or systematic biases az revealed by RNA-SeQC metrics was accomplished by including corresponding terms into statistical model using limma-voom methodology.

The association between baseline neutrophils and lymphocytes and EULAR response was further evaluated among biologic initiators that were not included in the initial cohorts studied (C1 and C2). These initiators were categorized into one of the four following groups based on the characteristics of their biologic initiation and line of therapy (naive vs experienced biologic use): 1) biologic-naive TNF initiator, 2) biologic-experienced TNF initiator, 3) biologic-naive non-TNF initiator, or 4) biologic-experienced non-TNF initiator. EULAR response at 3 month follow-up visit was evaluated and patients were further categorized as moderate to good EULAR response or poor EULAR response. Baseline (at time of initiation) neutrophils, lymphocytes and white blood counts (WBC) were available and from these baseline measures, the following ratios were calculated: 1) Neutrophil:lymphocyte log ratio=ln(neutrophils/lymphocytes), 2) Neutrophil:WBC log ratio=ln(neutrophils/WBC) and 3) Lymphocyte:WBC log ratio=ln(lymphocytes/WBC). Logistic regression was used to evaluate the association between baseline neutrophil:lymphocyte log ratio and EULAR response without covariate adjustment and adjusted by drug group and a priori selected variables (age at drug initiation, smoking status, years of disease duration at initiation, modified HAQ at initiation, concomitant MTX use at time of initiation of drug, number of prior biologics used at time of initiation). In a similar fashion, the association between baseline neutrophil:WBC log ratios and EULAR response, and lymphocyte:WBC log ratios and EULA response, were estimated.

Example 2 Molecular Signature of Anti-TNF Treatment

The genome-wide gene expression levels obtained prior to initiating anti-TNF therapy and the genome-wide expression levels obtained after 3 months of anti-TNF treatment were compared among patients in each cohort (C1 and C2), irrespective of the EULAR response status of the patients.

FIG. 1A shows the distribution of p-values for the differences in gene expression after 3 months of treatment relative to baseline for C1 and C2. Substantial numbers of genes achieved low False Discovery Rate (FDR) levels (775 genes at B-H FDR<0.05) in C1, but not in C2 (3 genes at BH-FDR<0.05). This result could possibly be explained by a lower power because of a smaller number of paired samples in C2 (n=32 in C1 vs. n=20 in C2).

FIG. 1B shows the mean differences in gene expression levels between the baseline and 3 month expression levels for C1 and C2, with certain genes highlighted (e.g., markers for myeloid, B, and T cells). A consistent anti-TNF treatment effect is manifested as a strong positive correlation in the mean differences of gene expression levels (at baseline vs. 3 months after treatment) observed for C1 and C2. A high level of correlation in the changes in gene expression between the baseline and 3 month expression levels was observed for both cohorts. The majority of genes exhibiting the largest differences between their month 3 and baseline levels in both cohorts were down-regulated and related to myeloid cells (see, Table 2). Granulocyte functions appeared to be prominently modulated with, in particular, functions related to degranulation, chemotaxis and migration. Platelet-related genes were also significantly down-regulated. The majority of the up-regulated genes were involved in protein synthesis, including transcription, translation and ribosome-related genes (see, Table 2). This result was confirmed by the analysis of the most significantly modulated cell surface markers (see, FIG. 1B). T and B cell marker (i.e. CD3, CD4, CD8, CD79, CD22 and CD52) were significantly up-regulated in both cohorts, while myeloid markers (CD14, CD55, CD46) were down-regulated.

TABLE 2 Gene ontology analysis of the genes modulated between baseline and 3-month following anti-TNF treatment Number GO ID Term of genes Direction p-value FDR GO:0042581 Specific granule 123 Down 0 0 GO:0035580 Specific granule lumen 43 Down 0 0 GO:0019730 Antimicrobial humoral response 37 Down 0 0 GO:0051852 Disruption by host of symbiont cells 9 Down 0 0 GO:0030667 Secretory granule membrane 215 Down 0   1E−07 GO:0051818 Disruption of cells of other organism 10 Down 0   1E−07 Involved in symbiotic interaction GO:0070820 Tertiary granule 129 Down 0   2E−07 GO:0101003 Ficolin-1-rich granule membrane 50 Down 0   2E−07 GO:0004875 Complement receptor activity 6 Down 0   3E−07 GO:0002251 Organ or tissue specific immune 11 Down 0   4E−07 response GO:0002385 Mucosal immune response 10 Down 0 0.000001 GO:0002227 Innate immune response in mucosa 9 Down 0  1.8E−06 GO:0051873 Killing by host of symbiont cells 7 Down 0  5.6E−06 GO:0051883 Killing of cells in other organism 8 Down 0  8.1E−06 involved in symbiotic interaction GO:1904724 Tertiary granule lumen 43 Down 0  8.7E−06 GO:0042119 Neutrophil activation 412 Down 0 0.000009 GO:0002446 Neutrophil mediated immunity 413 Down 0  9.1E−06 GO:0002283 Neutrophil activation involved in 406 Down 0  9.1E−06 immune response GO:0043312 Neutrophil degranulation 405 Down 0 1.03E−05 GO:1990266 Neutrophil migration 47 Down 0 1.04E−05 GO:0030141 Secretory granule 514 Down   1E−07 1.39E−05 GO:0036230 Granulocyte activation 416 Down   1E−07 1.43E−05 GO:0019731 Antibacterial humoral response 14 Down   1E−07 1.47E−05 GO:0030593 Neutrophil chemotaxis 40 Down   1E−07 1.47E−05 GO:0043299 Leukocyte degranulation 433 Down   1E−07 1.55E−05 GO:0019229 Regulation of vasoconstriction 11 Down   1E−07 2.13E−05 GO:0002444 Myeloid leukocyte mediated immunity 439 Down   1E−07 2.16E−05 GO:0002275 Myeloid cell activation involved in 436 Down   1E−07 2.55E−05 immune response GO:0045055 Regulated exocytosis 527 Down   2E−07 0.000029 GO:0097530 Granulocyte migration 58 Down   2E−07 3.26E−05 GO:0071621 Granulocyte chemotaxis 49 Down   2E−07 3.26E−05 GO:0099503 Secretory vesicle 562 Down   2E−07 3.48E−05 GO:1902622 Regulation of neutrophil migration 19 Down   3E−07 4.21E−05 GO:0031091 Platelet alpha granule 46 Down   4E−07 6.71E−05 GO:0005161 Platelet-derived growth factor receptor 8 Down   6E−07 8.59E−05 binding GO:0002274 Myeloid leukocyte activation 492 Down   7E−07 0.0001 GO:0006023 Aminoglycan biosynthetic process 43 Down   7E−07 0.0001 GO:0031424 Keratinization 19 Down  1.3E−06 0.000182 GO:0008146 Sulfotransferase activity 16 Down  2.1E−06 0.000276 GO:0035579 Specific granule membrane 73 Down  2.1E−06 0.000276 GO:0051923 Sulfation 6 Down  2.2E−06 0.000281 GO:0006887 Exocytosis 600 Down  2.3E−06 0.000297 GO:0006024 Glycosaminoglycan biosynthetic process 42 Down  2.9E−06 0.000363 GO:0097756 Negative regulation of blood vessel 18 Down 0.000003 0.000363 diameter GO:0050832 Defense response to fungus 16 Down 0.000003 0.000363 GO:0019915 Lipid storage 36 Down  3.4E−06 0.000406 GO:0070821 Tertiary granule membrane 59 Down  3.4E−06 0.000408 GO:0006022 Aminoglycan metabolic process 71 Down  4.1E−06 0.000478 GO:0006527 Arginine catabolic process 5 Down  4.8E−06 0.00056 GO:0010745 Negative regulation of macrophage 7 Down  5.1E−06 0.000587 derived foam cell differentiation GO:0010888 Negative regulation of lipid storage 11 Down  5.5E−06 0.000626 GO:0010743 Regulation of macrophage derived foam 13 Down  5.7E−06 0.000645 cell differentiation GO:0005520 Insulin-like growth factor binding 6 Down  7.2E−06 0.000773 GO:0072672 Neutrophil extravasation 6 Down  7.5E−06 0.000795 GO:1905953 Negative regulation of lipid localization 18 Down  7.6E−06 0.000809 GO:0097529 Myeloid leukocyte migration 79 Down  7.9E−06 0.000825 GO:0042310 Vasoconstriction 17 Down  8.9E−06 0.000917 GO:1902624 Positive regulation of neutrophil 16 Down  8.9E−06 0.000917 migration GO:0070268 Cornification 15 Down  9.7E−06 0.000997 GO:0061844 Antimicrobial humoral immune response 21 Down 1.04E−05 0.001029 mediated by antimicrobial peptide GO:0030203 Glycosaminoglycan metabolic process 67 Down 1.04E−05 0.001029 GO:0002576 Platelet degranulation 70 Down 1.15E−05 0.001111 GO:0019233 Sensory perception of pain 23 Down 1.44E−05 0.001379 GO:1903510 Mucopolysaccharide metabolic process 55 Down 1.71E−05 0.001634 GO:0002263 Cell activation involved in immune 530 Down 1.85E−05 0.001758 response GO:0002366 Leukocyte activation involved in 527 Down 1.88E−05 0.00176 immune response GO:0030730 Sequestering of triglyceride 8 Down 0.000021 0.001941 GO:0031225 Anchored component of membrane 34 Down 2.12E−05 0.001942 GO:0050542 Icosanoid binding 5 Down 2.47E−05 0.002253 GO:0031092 Platelet alpha granule membrane 13 Down 2.91E−05 0.002577 GO:0031640 Killing of cells of other organism 25 Down 2.99E−05 0.002603 GO:0090022 Regulation of neutrophil chemotaxis 17 Down 0.00003 0.002603 GO:0043691 Reverse cholesterol transport 6 Down 3.33E−05 0.002863 GO:0031093 Platelet alpha granule lumen 32 Down 3.41E−05 0.002902 GO:0051931 Regulation of sensory perception 8 Down 3.63E−05 0.002945 GO:0006954 Inflammatory response 345 Down 3.91E−05 0.003129 GO:0016755 Transferase activity, transferring amino- 7 Down 4.05E−05 0.003208 acyl groups GO:0046903 Secretion 875 Down 4.73E−05 0.0037 GO:0050996 Positive regulation of lipid catabolic 9 Down 5.66E−05 0.004355 process GO:0006940 Regulation of smooth muscle contraction 12 Down 5.74E−05 0.004394 GO:0018149 Peptide cross-linking 11 Down 5.83E−05 0.00445 GO:0032637 Interleukin-8 production 49 Down 6.14E−05 0.004667 GO:0050786 RAGE receptor binding 8 Down 7.33E−05 0.005498 GO:0032940 Secretion by cell 833 Down 7.38E−05 0.005515 GO:0030335 Positive regulation of cell migration 204 Down 7.58E−05 0.005638 GO:0010883 Regulation of lipid storage 25 Down 8.29E−05 0.00612 GO:0010742 Macrophage derived foam cell 17 Down 8.94E−05 0.006498 differentiation GO:0040017 Positive regulation of locomotion 222 Down 0.000092 0.006607 GO:0042742 Defense response to bacterium 100 Down 9.43E−05 0.006741 GO:0030198 Extracellular matrix organization 96 Down 9.69E−05 0.006828 GO:0000272 Polysaccharide catabolic process 18 Down 0.000103 0.007088 GO:0045907 Positive regulation of vasoconstriction 5 Down 0.000105 0.007107 GO:0015721 Bile acid and bile salt transport 9 Down 0.000106 0.007202 GO:2000147 Positive regulation of cell motility 206 Down 0.000116 0.007858 GO:0032496 Response to lipopolysaccharide 182 Down 0.000123 0.008212 GO:0035994 Response to muscle stretch 10 Down 0.000129 0.008445 GO:0034774 Secretory granule lumen 225 Down 0.000132 0.008571 GO:1903524 Positive regulation of blood circulation 16 Down 0.000137 0.008649 GO:0032677 Regulation of interleukin-8 production 45 Down 0.000139 0.008777 GO:0006805 Xenobiotic metabolic process 33 Down 0.000141 0.008879 GO:0001533 Cornified envelope 9 Down 0.000147 0.008905 GO:0045408 Regulation of interleukin-6 biosynthetic 10 Down 0.000157 0.009389 process GO:0071622 Regulation of granulocyte chemotaxis 25 Down 0.00016 0.009533 GO:0090136 Epithelial cell-cell adhesion 10 Down 0.00016 0.009533 GO:0009617 Response to bacterium 270 Down 0.000166 0.009841 GO:0006614 SRP-dependent cotranslational protein 88 Up 0 0 targeting to membrane GO:0006613 Cotranslational protein targeting to 93 Up 0 0 membrane GO:0022626 Cytosolic ribosome 95 Up 0 0 GO:0045047 Protein targeting to ER 97 Up 0 0 GO:0072599 Establishment of protein localization to 100 Up 0 0 endoplasmic reticulum GO:0003735 Structural constituent of ribosome 141 Up 0 0 GO:0044391 Ribosomal subunit 163 Up 0 0 GO:0022625 Cytosolic large ribosomal subunit 54 Up 0 0 GO:0070972 Protein localization to endoplasmic 116 Up 0 0 reticulum GO:0000184 Nuclear-transcribed mRNA catabolic 114 Up 0 0 process, nonsense-mediated decay GO:0042613 MHC class II protein complex 14 Up 0 0 GO:0005840 Ribosome 200 Up 0 0 GO:0015934 Large ribosomal subunit 103 Up 0 0 GO:0006612 Protein targeting to membrane 137 Up 0 0 GO:0022627 Cytosolic small ribosomal subunit 38 Up 0 0 GO:0006413 Translational initiation 172 Up 0 0 GO:0015935 Small ribosomal subunit 62 Up 0 0 GO:0006364 rRNA processing 223 Up 0 0 GO:0019083 Viral transcription 167 Up 0 0 GO:0019080 Viral gene expression 181 Up 0 0 GO:0042254 Ribosome biogenesis 276 Up 0 0 GO:0032395 MHC class II receptor activity 8 Up 0 0 GO:0016072 rRNA metabolic process 249 Up 0 0 GO:0042611 MHC protein complex 23 Up 0 0 GO:0044445 Cytosolic part 189 Up 0 0 GO:0000956 Nuclear-transcribed mRNA catabolic 189 Up 0 0 process GO:0002181 Cytoplasmic translation 53 Up 0 0 GO:0090150 Establishment of protein localization to 219 Up 0 0 membrane GO:0034470 ncRNA processing 323 Up 0 0 GO:0023026 MHC class II protein complex binding 15 Up 0 0 GO:0023023 MHC protein complex binding 17 Up 0 0 GO:0022613 Ribonucleoprotein complex biogenesis 396 Up 0 0 GO:0042255 Ribosome assembly 49 Up 0 0 GO:0042273 Ribosomal large subunit biogenesis 58 Up 0 0 GO:0019843 rRNA binding 51 Up 0   1E−07 GO:0034660 ncRNA metabolic process 444 Up 0   1E−07 GO:0006402 mRNA catabolic process 297 Up 0   8E−07 GO:0002396 MHC protein complex assembly 5 Up 0 0.000001 GO:0006401 RNA catabolic process 318 Up 0  1.3E−06 GO:1990904 Ribonucleoprotein complex 663 Up 0  2.5E−06 GO:0030529 Intracellular ribonucleoprotein complex 660 Up 0  2.7E−06 GO:0000027 Ribosomal large subunit assembly 24 Up 0  3.7E−06 GO:0006414 Translational elongation 105 Up 0  3.8E−06 GO:0006412 Translation 531 Up 0  3.8E−06 GO:0006605 Protein targeting 298 Up 0 0.000004 GO:0043043 Peptide biosynthetic process 543 Up 0  4.2E−06 GO:0042274 Ribosomal small subunit biogenesis 60 Up 0 0.000009 GO:0098553 Lumenal side of endoplasmic reticulum 25 Up   1E−07 1.47E−05 membrane GO:0070125 Mitochondrial translational elongation 79 Up   1E−07 2.29E−05 GO:0000028 Ribosomal small subunit assembly 15 Up   1E−07 2.73E−05 GO:0070126 Mitochondrial translational termination 80 Up   2E−07 0.000029 GO:0032543 Mitochondrial translation 110 Up   2E−07 3.26E−05 GO:0043604 Amide biosynthetic process 589 Up   2E−07 0.000042 GO:0140053 Mitochondrial gene expression 116 Up   3E−07 4.55E−05 GO:0005761 Mitochondrial ribosome 76 Up   4E−07 6.34E−05 GO:0006518 Peptide metabolic process 612 Up   4E−07 6.39E−05 GO:0006415 Translational termination 90 Up   6E−07 8.88E−05 GO:0005198 Structural molecule activity 318 Up   6E−07 9.45E−05 GO:0034655 Nucleobase-containing compound 391 Up   7E−07 0.000105 catabolic process GO:0031294 Lymphocyte costimulation 55 Up  1.3E−06 0.000178 GO:0046700 Heterocycle catabolic process 405 Up  1.4E−06 0.000187 GO:0031295 T cell costimulation 54 Up  1.5E−06 0.0002 GO:0019439 Aromatic compound catabolic process 410 Up  1.5E−06 0.0002 GO:0044270 Cellular nitrogen compound catabolic 407 Up  2.2E−06 0.000281 process GO:0003823 Antigen binding 37 Up  2.7E−06 0.000337 GO:1901361 Organic cyclic compound catabolic 417 Up  2.8E−06 0.000352 process GO:0005743 Mitochondrial inner membrane 361 Up  2.9E−06 0.000363 GO:0072657 Protein localization to membrane 349 Up  3.3E−06 0.000393 GO:0030669 Clathrin-coated endocytic vesicle 31 Up  3.6E−06 0.000423 membrane GO:0042605 Peptide antigen binding 18 Up  5.2E−06 0.000597 GO:0006396 RNA processing 752 Up  6.3E−06 0.000701 GO:0098800 Inner mitochondrial membrane protein 100 Up  9.9E−06 0.001004 complex GO:0022618 Ribonucleoprotein complex assembly 176 Up 1.01E−05 0.001026 GO:0005762 Mitochondrial large ribosomal subunit 47 Up 1.88E−05 0.00176 GO:0050851 Antigen receptor-mediated signaling 187 Up 2.06E−05 0.001917 pathway GO:0019866 Organelle inner membrane 393 Up 2.12E−05 0.001942 GO:0071826 Ribonucleoprotein complex subunit 186 Up 2.56E−05 0.002314 organization GO:0043603 Cellular amide metabolic process 706 Up 2.56E−05 0.002314 maturation of SSU-rRNA from GO:0000462 Tricistronic rRNA transcript (SSU- 33 Up 3.32E−05 0.002863 rRNA, 5.8S rRNA, LSU-rRNA) GO:0098798 Mitochondrial protein complex 119 Up 3.37E−05 0.002881 GO:0070469 Respiratory chain 75 Up 4.04E−05 0.003208 GO:0019886 Antigen processing and presentation of 72 Up 4.51E−05 0.003559 Exogenous peptide antigen via MHC class II GO:0002495 Antigen processing and presentation of 74 Up 4.57E−05 0.003593 peptide antigen via MHC class II GO:0071346 Cellular response to interferon-gamma 91 Up 5.17E−05 0.004031 GO:0030490 Maturation of SSU-rRNA 46 Up 6.55E−05 0.004937 GO:0005746 Mitochondrial respiratory chain 69 Up 8.37E−05 0.006151 GO:1904667 Negative regulation of ubiquitin protein 67 Up 8.87E−05 0.006497 ligase activity GO:0048027 mRNA 5′-UTR binding 19 Up 9.03E−05 0.006535 GO:0050852 T cell receptor signaling pathway 152 Up 9.07E−05 0.006535 GO:0060333 Interferon-gamma-mediated signaling 75 Up 0.000097 0.006828 pathway GO:0030684 Preribosome 63 Up 9.87E−05 0.006909 GO:0000470 Maturation of LSU-rRNA 19 Up 9.89E−05 0.006909 GO:0016071 mRNA metabolic process 673 Up 0.000101 0.00703 GO:0002504 Antigen processing and presentation of 75 Up 0.00012 0.008046 Peptide or polysaccharide antigen via MHC class II GO:0008135 Translation factor activity, RNA binding 65 Up 0.000142 0.008884 GO:0034663 Endoplasmic reticulum chaperone 10 Up 0.000147 0.008902 complex GO:0005759 Mitochondrial matrix 317 Up 0.000148 0.008956

Cell type-specific RNA-seq data was used to further investigate the cell types that were modulated by anti-TNF treatment. See, Linsley et al., PLoS ONE, 2014, 9(10):e109760, which is herein incorporated by reference in its entirety. FIG. 1C shows that neutrophil-related genes exhibited the largest (by absolute value) and most significant reduction in expression after 3 months of anti-TNF treatment in both cohorts (negative correlation with the effect of anti-TNF treatment). Conversely, genes specific to B cells, CD4 cells, and CD8 cells exhibited increased expression after 3 months of anti-TNF treatment in both cohorts (i.e., were positively correlated with the effect of anti-TNF treatment). The consistency of these results was validated using three other publicly-available cell type-specific datasets as a reference that were generated using microarrays. See, Abbase et al., Genes and Immunity, 2005, 6:319-331; and Allantaz et al., PLoS ONE, 2012, 7(1):e29979, which are herein incorporated by reference in their entirety. FIG. 1D shows that genes related to neutrophils were down-regulated, and genes related to B, CD4, and CD8 cells were up-regulated, after treatment in these cell type-specific datasets. In addition, complete blood count (CBC) analysis showed that, on average, the neutrophils/WBC ratio at month 3 is 87% of that at baseline (95% CI=83-91%; p=1.2*10−6) for C1 and 91% (95% CI=85-97%; p=0.004) for C2, across all patients studied (data not shown), further validating these results.

Protein expression levels in plasma samples was analyzed using shotgun proteomics. FIG. 1E shows the distribution of p-values for the differences in protein expression after 3 months of treatment relative to baseline for C1 and C2. Statistically significant differences between 3-month and baseline samples was detected in both cohorts (14 and 9 proteins at BH-FDR<0.05 in each cohort, permutation p<0.001 in both cohorts). FIG. 1F shows the average differences in protein expression levels between the baseline and 3 month expression levels for C1 and C2, with certain acute phase proteins highlighted. The average differences of protein expression levels between the baseline levels and 3-month follow-up levels showed a positive correlation across all proteins included in the analysis between the two cohorts, which was infrequently observed upon permutation (ρ=0.27, p=0.04). Similar to the gene expression results, analysis of gene ontology (GO) categories of the proteins modulated after anti-TNF treatment revealed a down-regulation of inflammatory pathways, although without discriminating between innate and adaptive immune processes (see, Table 3). Conversely, proteins mostly synthesized in the liver, including fibronectin (FN), plasminogen (PLG), apolipoprotein E (APOE) as well as proteins that are not involved in immune functions (i.e. SERPINF1/PEDF, HSPA5/BiP) were increased. Inclusion of less abundant proteins in the analysis resulted in the detection of haptoglobin and C-reactive protein (CRP), both well recognized positive acute phase proteins, which decrease by more than 50% (p≤0.01) in each cohort.

TABLE 3 Gene ontology analysis of the proteins modulated between baseline and 3-month of anti-TNF treatment Number GO ID Term of genes Direction p-value FDR GO:0006955 Immune response 61 Down 0.000576919 0.197346629 GO:0002252 Immune effector process 51 Down 0.00125068 0.197346629 GO:0002253 Activation of immune response 39 Down 0.001743467 0.197346629 GO:0050778 Positive regulation of immune 42 Down 0.002776652 0.197346629 response GO:0002920 Regulation of humoral immune 30 Down 0.003821307 0.197346629 response GO:0030449 Regulation of complement 29 Down 0.004224724 0.197346629 activation GO:0002376 Immune system process 65 Down 0.004342794 0.197346629 GO:0002684 Positive regulation of immune 46 Down 0.004881326 0.197346629 system process GO:0006959 Humoral immune response 37 Down 0.005334188 0.197346629 GO:2000257 Regulation of protein activation 30 Down 0.006739644 0.197346629 cascade GO:0002673 Regulation of acute inflammatory 31 Down 0.007416631 0.197346629 response GO:0002250 Adaptive immune response 21 Down 0.008777251 0.197346629 GO:0002443 Leukocyte mediated immunity 33 Down 0.009034413 0.197346629 GO:0050776 Regulation of immune response 47 Down 0.009143455 0.197346629 GO:0006956 Complement activation 33 Down 0.010827034 0.197346629 GO:0044437 Vacuolar part 8 Down 0.014156612 0.197346629 GO:0002020 Protease binding 7 Up 0.00195389 0.197346629 GO:0048589 Developmental growth 7 Up 0.005823682 0.197346629 GO:0033002 Muscle cell proliferation 5 Up 0.006239685 0.197346629 GO:0030182 Neuron differentiation 7 Up 0.007519171 0.197346629 GO:0030030 Cell projection organization 9 Up 0.009212066 0.197346629 GO:0051345 Positive regulation of hydrolase 7 Up 0.010586761 0.197346629 activity GO:0072359 Circulatory system development 18 Up 0.011941309 0.197346629 GO:1901362 Organic cyclic compound 10 Up 0.014384575 0.197346629 biosynthetic process GO:0019218 Regulation of steroid metabolic 5 Up 0.015171103 0.197346629 process

Thus, transcriptional and proteomics analyses after initiation of anti-TNF treatment confirmed a reduction of inflammatory pathways, with a marked reduction of myeloid-specific functions in both cohorts (C1 and C2). Proteomics analysis also showed a reduction pro-inflammatory markers, including complement and acute-phase proteins (See, Table 3). CRP also appeared to be down-regulated. Neutrophil functions, including degranulation, migration/chemotaxis and chemokine production were significantly down-regulated, as well as monocyte-specific pathways and platelet functions (see, Table 2). Conversely, markers of adaptive immune functions, including T cell markers and protein synthesis, were increased, which may be related to the overall decrease in myeloid transcripts.

Example 3 Assessing Association Between the Molecular Signature of Anti-TNF Treatment and Response to Anti-TNF Treatment

To determine whether the molecular signature of anti-TNF is reflective of the clinical response of RA patients, and can therefore be used to predict the probability and/or degree to which a patient will respond to anti-TNF therapy, differences in gene expression levels between 3 months and baseline (MO3-BL) were estimated separately for the good responders and the poor responders in each cohort (C1 and C2). The significance of Spearman correlation coefficients for differences in gene expression for each set of subjects was estimated by permutation. FIG. 2A shows the comparison of the differences in gene expression levels (MO3-BL) between pairs of each of the groups of patients (comparing C1 good and C1 poor responders; C1 good and C2 good responders; C1 good and C2 poor responders; C2 good and C1 poor responders; C2 good and C2 poor responders, and C1 poor and C2 poor responders). Except for the poor responders from the C2 cohort, the remaining three groups of subjects (the good and poor responders in C1, and the good responders in C2) displayed a significant correlation in MO3-BL differences in each of the comparisons. These results suggested that there were similar changes in gene expression (MO3-BL) of individual genes in the good and poor responders of the cohorts. The low discrepancy in MO3-BL differences in gene expression between responders and poor responders was further confirmed by analyzing pathways modulated in response to anti-TNF treatment, using gene ontology (GO) categories (see, Table 2; data not shown).

Analysis of 3 months and baseline differences (MO3-BL) using shotgun plasma proteomics corroborated the gene expression findings. FIG. 2B shows comparisons in the differences in protein expression levels (MO3-BL) between the good and poor responders in C1, and the good and poor responders in C2, respectively. Changes in protein expression after anti-TNF treatment were positively correlated (ρ=0.48, p=0.0029 for C1; ρ=0.34, p=0.0079) between good and poor responders in both cohorts. FIG. 2C shows differences in the protein expression levels (Mo3-BL) in pathways that are modulated after anti-TNF expression, using gene ontology categories (see, Table 3), in the good and in the poor responders in C1 and C2. It was not possible to discriminate between good and poor responders based on the expression of proteins in these pathways. Changes in cell populations by complete blood count (CBC) analysis showed a greater decrease in the neutrophils/WBC ratio from baseline to 3-month in good responders than in poor responders in both cohorts (by 10% and 6% in the C1 and C2 cohorts, respectively). These results were only statistically significant for C1 (p=0.03; 95% CI=[−19%, −1.6%]), and not for C2 (p=0.30; 95% CI=[−18%, 5.9%]).

Overall, the results indicated that the molecular signature of anti-TNF was not closely correlated with whether the RA patients in C1 and C2 were good or poor responders. Additional factors are probably involved in the development of demonstrable clinical responses to anti-TNF treatment.

Example 4 Analysis of Gene Expression Prior to Anti-TNF Treatment (At Baseline)

Gene expression in the good and poor responders of C1 and C2 prior to anti-TNF treatment (at baseline) was compared to determine whether baseline gene expression levels could be used to predict whether a patient would respond well (or poorly) to anti-TNF treatment. FIG. 3A shows the distribution of p-values for the differences in gene expression between good responders and poor responders prior to anti-TNF treatment (at baseline) in C1 and C2. Only modest differences in gene expression between the good and poor responders were demonstrated. Differences between gene expression levels achieved statistical significance in C1 (77 and 536 genes at BH-FDR cutoffs of 0.1 and 0.2 respectively) but not in C2 (lowest BH-FDR of 0.73). FIG. 3B shows the differences in baseline gene expression levels between the good and poor responders in C1 and C2. The gene expression levels correlated positively between the two cohorts, but no statistical significance was achieved by permutation control (ρ=0.21; p=0.45).

FIG. 3C shows a comparison of the baseline gene expression differences between the good and poor responders for the 10% most variable genes (i.e., those genes whose expression varied the most across baseline samples on average between C1 and C2) in C1 and the 10% most variable genes in C2, with certain genes highlighted (e.g., cell surface markers for myeloid cells and lymphocytes). Comparing the 10% most variable genes between good and poor responders in each cohort resulted in a higher correlation between the cohorts. Genes for cell surface markers that are associated with myeloid cells (CD14, CD36, CD46, CD47, CD163, and CD164) were expressed at higher levels on average in good responders in both cohorts prior to anti-TNF treatment (at baseline), while genes for surface markers for lymphocytes, including T cells (CD52, CD48, CD3D, CD8A) and B-cells (CD79B, CD22), were expressed at higher levels on average in poor responders in both cohorts prior to anti-TNF treatment (at baseline). This result suggested that good and poor responders exhibited differences in their immune systems prior to anti-TNF treatment (i.e., at baseline), including in the number or characteristics of their myeloid and lymphocyte cells.

The cell type-specific RNA-seq methodology (described with FIG. 1C) was used to further understand the immune system differences between good and poor responders at baseline. FIG. 4A shows the average gene expression at baseline for subsets of genes associated with particular cell types (neutrophils, monocytes, B-cells, CD4 cells, CD8 cells, and NK cells) in good responders compared to poor responders in C1 and C2. Subsets of the top 10 genes (FIG. 7), top 50 genes (FIG. 8), or top 250 genes (FIG. 9), based on their expression levels in the particular cell types, were used, as determined using a reference cell-type specific data set. See, Linsley et al., PLoS ONE, 9(10):e109760. Genes that are most expressed in innate immune cells (neutrophils and monocytes) were, on average, found to be expressed at higher levels in good responders, while genes predominantly expressed in the adaptive compartment (CD4/CD8/NK/B-cells) were on average expressed at higher levels in poor responders. Thus, RA patients that will exhibit good response to anti-TNF treatment appear to likely have more innate immune cells (including neutrophils and monocytes) prior to anti-TNF treatment, while RA patients that will exhibit poor response to anti-TNF treatment likely have more adaptive immune cells prior to anti-TNF treatment. This observation was statistically significant as estimated by permutation and assessed across both cohorts (p=0.03), and interesting given the limited conservation observed between C1 and C2 in the broader transcriptional analysis of FIG. 3A. The results were further confirmed by performing a similar analysis using five publicly available RA datasets containing gene expression data at baseline for responders and non-responders to anti-TNF therapy. See, Julia et al., PLoS ONE, 2009, 4(10):e7556; Bienkowska et al., Genomics, 2009-94:423-432; Toonen et al., PLoS ONE, 2012, 7(3):e33199; Mesko et al., Genome Medicine, 2013, 5:59; and Maclsaac et al., PLoS ONE, 2014, 9(12):e113937, which are herein incorporated by reference in their entirety. FIG. 4B shows that despite differences in study designs, three datasets display qualitatively similar results wherein, on average, genes elevated in the innate compartment were expressed at higher levels in good responders and genes elevated in adaptive compartment were expressed at higher levels in poor responders.

Thus, at baseline, innate immune cell types were on average expressed at higher level in good responders from both cohorts, while the adaptive immune cell types were on average expressed at a higher level in poor responders (see, FIG. 3C and FIG. 4A). This observation was confirmed in three publicly available datasets after applying cell-type specific gene expression analysis (see, FIG. 4B). The reproducibility of the observation, despite the differences in the underlying studies that produced the datasets (including in patient selection and sample processing), shows that the make-up of the immune cells in subjects with RA is an important feature in determining response to anti-TNF therapies.

Example 5 Baseline Immune Cells as Predictors of Anti-TNF Treatment Response

Since the subset of genes evaluated in the above examples represent immune cell types present in blood, clinical information on blood cell types (neutrophil, lymphocyte and WBC counts) present in 2011 patients were analyzed to determine whether it can be predictive of RA patient response to anti-TNF therapy. Logistic regression models were set up to evaluate the probability that RA patients would exhibit a good or moderate EULAR response 3 months after starting anti-TNF therapy, as a function of their baseline neutrophil to lymphocyte log ratio [NLR], neutrophil to white blood cell (WBC) log ratio [NWR], or lymphocyte to WBC log ratio [LWR]. Three separate models (NLR, NWR, and LWR) were established for 2011 patients for whom the number of neutrophils, lymphocytes and WBCs were determined prior to anti-TNF treatment (at baseline) by complete blood count (CBC), and whose EULAR response was determined at a follow-up visit 3 month after anti-TNF treatment. The patients were evaluated, either without adjustment, or by adjusting for multiple variables, including the type of biologic received (Humira®/Remicade®, other anti-TNF biologic, or other non-anti-TNF biologic), patient experience with biologics (biologic naive vs. experienced), and other covariates (e.g., age, disease duration, smoking status, disability index, erosions, methotrexate treatment and number of prior biologics).

Readouts from linear regression models depict the probability of an RA patient exhibiting a good response as a function of neutrophil to lymphocyte ratio, neutrophil to WBC ratio, or lymphocyte to WBC ratio. The results of the first model showed that a one-unit increase in baseline NLR log ratio resulted in approximately a 20% increased probability of moderate to good EULAR response (1.23 increased probability) (unadjusted OR=1.23, 95% CI=1.06, 1.42; adjusted OR=1.20, 95% CI=1.03, 1.41). The effect is equivalent to concomitant methotrexate (MTX) treatment (odds ratio of MTX to good/moderate response=1.23 [95% CI=1.02-1.49; p=0.03]), which is used as a first-line therapy. The importance of neutrophils to anti-TNF response was confirmed by the second model, where a one-unit increase in baseline NWR log ratio resulted in a 1.9 increased probability of moderate or good EULAR response (unadjusted OR=1.91, 95% CI=1.14, 3.18; adjusted OR=1.72, 95% CI=1.01, 2.96). Conversely, the association between increased lymphocytes at baseline and non-response to anti-TNF therapy was emphasized by a 24% decreased probability of moderate or good EULAR response, following a one-unit increase in baseline LWR log ratio (unadjusted OR=0.76, 95% CI=0.62, 0.93; adjusted OR=0.77, 95% CI=0.62, 0.95). Thus, significant associations between NLR, NWR and LWR log ratios and EULAR response were observed.

The results of these models are consistent with the gene and protein expression observations described in the above examples. FIG. 5 shows a correlation between average baseline expression profiles of genes that are predominantly expressed in neutrophils, B cells, CD4 cells, CD8 cells, monocytes and NK cells in C1 and C2, compared to the corresponding counts of the cells and their ratios. Good responders have on average a higher fraction of innate immune cells at baseline, while poor responders have on average a higher fraction of adaptive immune cells at baseline. FIG. 5 thus shows that cell-type specific genes correlate with corresponding cell counts. Thus, clinical laboratory metrics at baseline can be useful readouts to assess predictability of response to anti-TNF treatment in RA patients. Determining the neutrophil to lymphocyte ratio, or normalized lymphocyte or neutrophil counts (i.e., lymphocyte/WBC or neutrophil/WBC ratios), in a subject with RA prior to anti-TNF treatment (baseline) using CBC can be used to predict whether that subject will respond to anti-TNF treatment.

Table 4 summarizes the probability of response and non-response in a subset of the 76 patients (in C1 and C2) in the study described above based on the log ratio of neutrophils to lymphocytes (Ln(NRL)) at baseline. Table 5 presents the distribution of Ln(NRL) values in the dataset of 76 patients (in C1 and C2).

FIG. 6, FIG. 7 and FIG. 8 is genes that can be used as markers of innate immune cells and genes that can be used as markers of adaptive immune cells.

TABLE 4 Probability of Response and Non-Response to Anti-TNF Therapy Average of dataset (76 patients) 53% probability of response 47% probability of non-response Percentile Ln (NLR) Good responder Poor responder Total Comment <15 More adaptive <0.579 4 8 12 patients 67 percent probability of non-response >85 More innate >1.842 9 3 12 patients 75 percent probability of response <20 More adaptive <0.633 6 9 15 patients 60 percent probability of non-response >80 More innate >3.549 11 4 15 patients 73 percent probability of response <25 More adaptive <0.706 7 12 19 patients 63 percent probability of non-response >75 More innate >1.364 13 6 19 patients 68 percent probability of response <30 More adaptive <0.757 9 14 23 patients 61 percent probability of non-response >70 More innate >1.283 15 8 23 patients 65 percent probability of response Percentile represents the percentile of observed Ln (NLR) values in our dataset. e.g <15 represents patients having low NLR values lower than the 15th percentile of observed values >85 represents patients having high NLR values, greater than the 85th percentile of values observed Average of dataset represents the average probability of response or non-reponse when picking at random from our dataset, given the distribution of responders and non-responders in this dataset

TABLE 5 Distribution of Ln(NRL) Values Percentile of patients represented by NLR value Ln(NLR) value 10% 0.479 15% 0.579 20% 0.633 25% 0.706 30% 0.757 40% 0.841 50% 0.916 60% 1.136 70% 1.283 75% 1.364 80% 1.549 85% 1.642 90% 1.771 Minimum value observed 0.116 in our dataset Maximum value observed 3.038 in our dataset

Genes that can be used as markers of innate immune cells (higher expression in neutrophils and monocytes versus T cells and B cells) include those in the column labeled Innate in FIG. 6. Genes that can be used as markers of adaptive immune cells (higher expression in T cells and B cells versus neutrophils and monocytes) include those in the column labeled Adaptive in FIG. 6.

Other Embodiments

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

1. A method for treating a patient with rheumatoid arthritis, comprising:

detecting a ratio of innate immune cells to adaptive immune cells in a biological sample from the patient suffering from rheumatoid arthritis, wherein the detecting step comprises determining the value of the neutrophil to lymphocyte log ratio (ln[NLR]) in the biological sample; and
if the value of ln[NLR] is greater than 1.3, then administering to the patient an anti-TNF therapeutic, and if the value of ln[NLR] is 1.3 or below, then administering to the patient a rheumatoid arthritis therapeutic other than an anti-TNF therapeutic, thereby treating the patient.

2. A method of treating rheumatoid arthritis in a subject comprising:

determining that a ratio of innate immune cells to adaptive immune cells in a sample from the subject is high, wherein the determining step comprises determining that value of the neutrophil to lymphocyte log ratio (ln[NLR]) in the sample is greater than 1.3; and
administering an anti-TNF therapeutic to the subject.

3. A method for treating a patient with rheumatoid arthritis, comprising:

obtaining or having obtained a biological sample from the patient; and
performing or having performed an assay on the biological sample to determine a ratio of innate immune cells to adaptive immune cells,
wherein the determining step comprises determining the value of the neutrophil to lymphocyte log ratio (ln[NLR]) in the biological sample; and
if the value of ln[NLR] is greater than 1.3, then administering to the patient an anti-innate immune cell therapeutic agent selected from the group consisting of infliximab, adalimumab, golimumab, certolizumab pegol, and etanercept, and if the value of ln[NLR] is 1.3 or below, then administering a rheumatoid arthritis therapeutic other than infliximab, adalimumab, golimumab, certolizumab pegol, and etanercept, thereby treating the patient.

4. A method for treating a patient with rheumatoid arthritis, comprising:

detecting a ratio of innate immune cells to adaptive immune cells in a biological sample from the patient suffering from rheumatoid arthritis, wherein the detecting step comprises determining the value of the neutrophil to lymphocyte log ratio (ln[NLR]) in the biological sample; and
if the value of ln[NLR] is greater than 1.3, then administering to the patient an anti-innate immune cell therapeutic agent selected from the group consisting of infliximab, adalimumab, golimumab, certolizumab pegol and etanercept; and if value of ln[NLR] is 1.3 or below, then administering to the patient a rheumatoid arthritis therapeutic other than infliximab, adalimumab, golimumab, certolizumab pegol and etanercept, thereby treating the patient.

5. The method of claim 1, wherein the anti-TNF therapeutic is an anti-TNF antibody.

6. The method of claim 1, wherein the anti-TNF therapeutic is selected from the group consisting of: infliximab, adalimumab, golimumab, certolizumab pegol, and etanercept.

7. The method of any of claims 1, 3, or 4, wherein the rheumatoid arthritis treatment other than an anti-TNF therapeutic is selected from the group consisting of: abatacept, rituximab and tocilizumab.

8. The method of any of claims 1, 2, 3, or 4, wherein the step of determining the value of the neutrophil to lymphocyte log ratio (ln[NLR]) in the biological sample comprises determining the expression in the biological sample of one or more of: CD14, CD36, CD46, CD47, CD163, CD164, CD52, CD48, CD3D, CD8A, CD79D, and CD22.

9. The method of claim 2, wherein the anti-TNF therapeutic is an anti-TNF antibody.

10. The method of claim 2, wherein the anti-TNF therapeutic is selected from the group consisting of: infliximab, adalimumab, golimumab, certolizumab pegol, and etanercept.

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Patent History
Patent number: 11639923
Type: Grant
Filed: Sep 18, 2018
Date of Patent: May 2, 2023
Patent Publication Number: 20200256851
Assignee: MOMENTA PHARMACEUTICALS, INC. (Titusville, NJ)
Inventors: Ishan Capila (Ashland, MA), Victor Farutin (Watertown, MA), Thomas Prod'homme (Somerville, MA), Kevin McConnell (Branford, CT), Leona Ling (Winchester, MA)
Primary Examiner: Changhwa J Cheu
Application Number: 16/648,955
Classifications
International Classification: G01N 33/50 (20060101); C07K 16/24 (20060101);